Super resolution (SR) methods can be classified under two main categories: multiframe super-resolution (MFSR) and single-frame super-resolution (SFSR). MFSR methods estimate a high-resolution (HR) image (or a set of HR images) from a diverse set of low-resolution (LR) images. The nature of such LR diversity is what determines the applicability of any given MFSR method. The reconstruction-based MFSR techniques assume that relative scene motion is what gives rise to LR image diversity.
The most challenging aspect of these classical methods is the necessity of accurate estimation of motion patterns. The majority of such methods restrictively assume that the relative scene motion is translational. Some researchers offer more generalizations by incorporating rotational, affine and projective motion in their model. Recently, C. Liu and D. Sun, “On bayesian adaptive video super resolution,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 2, pp. 346-360, 2014, proposed an adaptive motion estimation solution with superior performance at an upscaling factor of ×4, but even this advanced reconstruction-based algorithm remains sensitive to non-smooth motion and strong aliasing (aliasing is the main challenge in motion estimation, and it can be severe, even at a relatively high sampling rate, if the underlying signal has significant high frequency content).
To avoid the need for motion estimation altogether, the authors of the MFSR methods of, e.g., M. Protter, M. Elad, H. Takeda, and P. Milanfar, “Generalizing the nonlocal-means to super-resolution reconstruction,” IEEE Transactions on Image Processing, vol. 18, no. 1, pp. 36-51, 2009 and H. Takeda, P. Milanfar, M. Protter, and M. Elad, “Super-resolution without explicit subpixel motion estimation,” IEEE Transactions on Image Processing, vol. 18, no. 9, pp. 1958-1975, 2009, implement image self-similarity tools, where spatiotemporal similarity between neighboring pixels is relied upon to estimate the missing pixels, with excellent results (up to ×3 upscaling).
When the number of LR images is too low for any multiframe method to work satisfactorily, SFSR becomes the only option. Example-based SFSR has attracted much attention after adopting the signal sparsity paradigm to estimate the HR image from its LR version. In this case, patches of the HR image are estimated by finding their (sparse) representations in terms of a database of example images, with surprisingly good performance (considering only one LR version of the HR image is available).